DDoS Detection Algorithm Based on Fuzzy Logic

While internet technologies are developing day by day, threats against them are increasing at the same speed. One of the most serious and common types of attacks is Distributed Denial of Service (DDoS) attacks. The DDoS intrusion detection approach proposed in this study is based on fuzzy logic and entropy. The network is modeled as a graph and graphics-based features are used to distinguish attack traffic from non-attack traffic. Fuzzy clustering is applied based on these properties to indicate the tendency of IP addresses or port numbers to be in the same cluster. Based on this uncertainty, attack and non-attack traffic were modeled. The detection stage uses the fuzzy relevance function. This algorithm was tested on real data collected from Bogazici University network.